Hi everyone! I'm looking for recommendations on where to learn more about Data Governance, especially from a strategic and requirements perspective, rather than just technical-focused content. Any good courses, training programs, or resources that dive deeper into governance frameworks and best practices? I’d appreciate your insights!

https://www.rnz.co.nz/news/business/527419/inland-revenue-giving-thousands-of-taxpayers-details-to-social-media-platforms-for-ad-campaigns?fbclid=IwY2xjawFLSwJleHRuA2FlbQIxMAABHfiQoZd2lKNuLPKWDo5IrGSrtYtTKNwWBrS0kfJBtccVTTWP9FPKrjY3zg_aem_jp9YxiznNYVeVO5Oo9wqfA

There are so many tools out there, but I found this video on YouTube. It did a good job of breaking down the essentials. Here's a quick list of the tools mentioned: - Basics: SQL, Python, Linux, bash scripting, network understanding - Technical Basics: Git, SFTP, PGP - Databases: PostgreSQL, MySQL, MongoDB - Data Platforms: Snowflake, Databricks, BigQuery, Redshift, Azure Synapse Analytics - Orchestration, ETL & Data Pipelines: Airflow, Dagster, SSAS, Azure Data Factory - Cloud: AWS, Azure, GCP - Others: Docker, Kubernetes, Terraform If you had to give advice on where to start, what would that be? What are your favored tools?

Excited to be presenting alongside @Christof Wenzeritt at Data Dreamland! Main topic is Data Mesh and how to do one. It offers an effective approach for managing large-scale data, but the transition isn't always smooth sailing. Join us when we delve into the challenges of designing ownership structures within a Data Mesh framework. We'll explore how to achieve the optimal balance between technical expertise and organizational design. Let's share our knowledge and navigate the exciting world of Data Mesh together! Get your ticket here: https://scalefr.ee/d7goui

At some point in each Data Vault project, the source data is analyzed, and a Data Vault 2.0 model is drafted. I saw a lot of different locations where teams draft their Data Vault model, Excel sheets, drawing tools like Draw.io or Miro.com, dedicated modeling tools, directly inside their automation tools, or on pen and paper. My past experience highlighted three most important things to consider when deciding where to model: - Must fit into the tool stack: if your automation tool offers Data Vault modeling, do it in there - Ease of use: Simplicity is important to streamline initial modeling, changes and additions - Persistent and centralized: All components of the whole model should be stored in a central place, to help other modelers identify what has been done already. Where are you currently designing the Data Vault model? What are your experiences? Let me know!